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Background: Objective training load (TL) indexes used in resistance training lack physiological significance. This study was aimed to provide a muscle physiology-based approach for quantifying TL in resistance exercises (REs).

Methods: Following individual torque-velocity profiling, fifteen participants (11 healthy males, stature: 178.

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Plug-and-play myoelectric control via a self-calibrating random forest common model.

J Neural Eng

January 2025

School of Informatics, The University of Edinburgh, 10 Chricton Street, Edinburgh, EH8 9LE, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND.

Objective: Electromyographic (EMG) signals show large variabilities over time due to factors such as electrode shifting, user behaviour variations, etc., substantially degrading the performance of myoelectric control models in long-term use. Previously one-time model calibration was usually required each time before usage.

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Context: Intermittent floor trunk extensions are popular exercises in group fitness programs. The aim of this study was to investigate whether fewer repetitions of longer isometric trunk extension efforts compared with more repetitions of shorter isometric contractions have different acute effects on muscle thickness and activation as well as perceived exertion.

Design: This study followed a cross-sectional design.

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Objective: To explore the effect of combined rehabilitation training and transcutaneous vagus nerve electrical stimulation (t-VNS) on promoting central nervous system remodeling and neurological function recovery in stroke patients.

Methods: A total of 124 S patients admitted to our hospital from January to December 2023 were included in this study. The therapeutic effects were evaluated using the Modified Barthel Index (MBI) and the simplified Fugl-Meyer Assessment Scale (sFMA) to measure patients' activities of daily living and motor function recovery.

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Myoelectric control has emerged as a promising approach for a wide range of applications, including controlling limb prosthetics, teleoperating robots and enabling immersive interactions in the Metaverse. However, the accuracy and robustness of myoelectric control systems are often affected by various factors, including muscle fatigue, perspiration, drifts in electrode positions and changes in arm position. The latter has received less attention despite its significant impact on signal quality and decoding accuracy.

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